Papers with rule-based
Surface Realization Shared Task 2019 (MSR19): The Team 6 Approach (D19-63)
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| Challenge: | This paper describes the approach developed by the Tilburg University team for the shallow track of the Multilingual Surface Realization Shared Task 2019 (SR'19). |
| Approach: | They propose a method for the shallow track of the Multilingual Surface Realization Shared Task 2019 using a rule-based and a statistical machine translation (SMT) model. |
| Outcome: | The proposed approach can generate texts in 11 languages, compared with the submission of the same approach for the same task in 2018 which only covered 6 languages. |
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension (2020.tacl-1)
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| Challenge: | ''Language is, at best, a means of directing others to construct similar-thoughts from their own prior knowledge,'' says K. S. Adams and Bruce. |
| Approach: | They present a free-form multiple-choice Chinese machine reading Comprehension dataset (C3) containing 13,369 documents and their associated 19,577 multiple-CHOice free- form questions. |
| Outcome: | The proposed model outperforms human models on linguistic, domain-specific, and general world knowledge problems. |
Sentence Level Temporality Detection using an Implicit Time-sensed Resource (L18-1)
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| Challenge: | Temporal sense detection of any word is an important aspect for detecting temporality at the sentence level. |
| Approach: | They build a temporal resource based on a semi-supervised learning approach . they use past, present, future, neutral and atemporal senses to tag sentences . |
| Outcome: | The proposed resource is based on a semi-supervised learning approach . it is used to tag sentences with past, present and future temporal senses . |
Improving Formality Style Transfer with Context-Aware Rule Injection (2021.acl-long)
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| Challenge: | Existing language models pre-trained on large-scale corpora often do not work well for user-generated data where the language styles differ significantly from the mainstream text. |
| Approach: | They propose a method for formality style transfer by injecting multiple rules into an end-to-end BERT-based encoder and decoder model. |
| Outcome: | The proposed method outperforms existing rule-based FST approaches on tweet sentiment analysis tasks. |
Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL (2022.coling-1)
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| Challenge: | Existing attempts on Text-to-SQL task show a dramatic decline in performance for new databases. |
| Approach: | They propose a hybrid system that integrates rule-based and deep learning components to improve model accuracy. |
| Outcome: | The proposed system achieves double-digit percentage improvement for non-Spider databases. |
Non-neural Models Matter: a Re-evaluation of Neural Referring Expression Generation Systems (2022.acl-long)
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| Challenge: | In recent years, neural models have outperformed rule-based and classic approaches in NLG. |
| Approach: | They evaluate two English datasets and evaluate their performance using automatic and human evaluations. |
| Outcome: | The proposed model outperforms rule-based and classic approaches on two English datasets and is compared with human-based models. |
New Datasets and Controllable Iterative Data Augmentation Method for Code-switching ASR Error Correction (2023.findings-emnlp)
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| Challenge: | In bilingual or multilingual settings, code-switching ASR has greater challenges and research value. |
| Approach: | They propose a controllable iterative method for improving the performance of mainstream automatic speech recognition systems by using Chinese-English code-switching dialogues. |
| Outcome: | The proposed method achieves the best performance compared with the rule-based, back-translation-based data augmentation methods and large language model ChatGPT. |
A Rewriting Approach for Gender Inclusivity in Portuguese (2023.findings-emnlp)
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| Challenge: | Recent research has focused on gender-inclusive and gender-neutral language . however, current approaches to gender- neutral rewriting for gendered languages rely on large datasets . |
| Approach: | They propose a rule-based and a neural-based tool for gender-neutral rewriting for Portuguese, a heavily gendered Romance language. |
| Outcome: | The proposed model fine-tunes large multilingual machine translation models on examples generated by the rule-based model. |
NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender Neutral Alternatives (2021.emnlp-main)
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| Challenge: | Recent years have seen an increasing need for gender-neutral and inclusive language. |
| Approach: | They propose a rule-based and a neural approach to gender-neutral rewriting for English . they use manually curated synthetic and natural data to train a rewriter . |
| Outcome: | The proposed approach improves on the rule-based approach with word error rates below 0.18% on synthetic, in-domain and out-domain test sets. |
Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations (2024.emnlp-main)
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| Challenge: | Existing work on citation generation has focused on unambiguous settings with single answers, failing to address the complexity of real-world scenarios. |
| Approach: | They propose a task of QA with source citation in ambiguous settings where multiple valid answers exist, where multiple sources exist. |
| Outcome: | The proposed framework generates multiple answers and cites their sources, allowing users to verify the factuality of each answer and make informed decisions. |
From Individual to Common: An Early Exploration of Consensus in Non-verifiable Data for Balanced Preference Optimization (2026.acl-long)
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| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) has demonstrated remarkable effectiveness in boosting the objective performance of Large Language Models (LLMs). |
| Approach: | They propose a dataset where response pairs differ only by subtle nuances and a model with a non-verifiable dataset. |
| Outcome: | The proposed model outperforms models trained on data with explicit quality gaps while maintaining objective capabilities. |